The Poverty Alleviation Model of Local Government Inclusive Innovation: A Case Study on Contiguous Poor Regions in China
Bibliographic record
Abstract
Due to the natural, historical, ethnic, religious, political, social and other reasons, the destitute areas covering China’s poor 70% , economic growth driven function is not strong, the conventional means of poverty alleviation work slowly, poverty alleviation and development cycle is longer, poverty is still serious. 14 destitute areas now determined have become the main battle field of poverty alleviation in China. Contiguous poor regions have experienced decades of development, the problems of survival, food and clothing of rural residents have been basically solved. The remarkable achievements have been gained in education, health care, public services, and environmental protection. But there are still several critical problems which hinder its development. This paper sums up these problems from micro, meso and macro aspects, and analyzes the reasons of them. This paper builds a theoretical framework to analyze inclusive innovation of poverty alleviate for local government. Inclusive innovation as a new theory aims to get more performance for less cost for more people. Inclusive innovation is to promote economic development results to benefit the majority of society engines, is to promote sustainable economic development and effective way. Inclusive innovation is to make all of the people, especially disadvantaged groups to participate in innovation activities, so that innovations spread to all of the people, to increase people’s opportunities for innovation and creativity, and make everyone benefit from innovation activities. Through the expansion of productive employment, increase poverty-stricken areas of infrastructure and investment in education and human resource development and other means, people can balance sharing opportunities, enhance the ability to escape poverty and be rich.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".